Public debt forecasts and machine learning: the Italian case

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dc.contributor.authorSica, Edgardo
dc.contributor.authorAltinbas, Hazar
dc.contributor.authorMarini, Gaetano Gabriele
dc.date.accessioned2024-03-13T10:35:38Z
dc.date.available2024-03-13T10:35:38Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractPurposePublic debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.Design/methodology/approachUsing quarterly observations over the period 2000-2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.FindingsThe results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.Originality/valueCompared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.en_US
dc.identifier.doi10.1108/JES-07-2023-0337
dc.identifier.issn0144-3585
dc.identifier.scopus2-s2.0-85180244941en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1108/JES-07-2023-0337
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4526
dc.identifier.wosWOS:001129325700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofJournal Of Economic Studiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPublic debten_US
dc.subjectForecastingen_US
dc.subjectRandom foresten_US
dc.subjectMachine learningen_US
dc.subjectItalyen_US
dc.subjectH63en_US
dc.subjectH68en_US
dc.subjectE17en_US
dc.titlePublic debt forecasts and machine learning: the Italian caseen_US
dc.typeArticleen_US

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